The material below is an excerpt from a book I am writing with Navin Williams and Sue York on Mobile Market Research, but its implications are much wider and I would love to hear people’s thoughts and suggestions.

Most commercial fields have methods of gaining and assessing insight other than market research, for example testing products against standards or legal parameters, test launching, and crowd-funding. There are also a variety of approaches that although used by market researchers are not seen by the market place as exclusively (or even in some cases predominantly) the domain of market research, such as big data, usability testing, and A/B testing.

The mobile ecosystem (e.g. telcos, handset manufacturers, app providers, mobile services, mobile advertising and marketing, mobile shopping etc) employs a wide range of these non-market research techniques, and market researchers working in the field need to be aware of the strengths and weaknesses of these approaches. Market researchers need to understand how they can use the non-market research techniques and how to use market research to complement what they offer.

The list below cover techniques frequently used in the mobile ecosystem which are either not typically offered by market researchers or which are offered by a range of other providers as well as market researchers. Key items are:

Usage data, for example web logs from online services and telephone usage from the telcos.

A/B testing.

Agile development.

Crowdsourcing, including open-source development and crowdfunding.

Usability testing.

Technology or parameter driven development.

Usage data

The mobile and online worlds leave an extensive electronic wake behind users. Accessing a website tells the website owner a large amount about the user, in terms of hardware, location, operating system, language the device is using (e.g. English, French etc), and it might make an estimate of things like age and gender based on the sites you visit and the answers you pick. Use a mobile phone and you tell the telco who you contacted, where you were geographically, how long the contact lasted, what sort of contact was it (e.g. voice or SMS). Use email, such as Gmail or Yahoo, and you tell the service provider who you contacted, which of your devices you used, and the content of your email. Use a service like RunKeeper or eBay or Facebook and you share a large amount of information about yourself and in most cases about other people too.

In many fields, market research is used to estimate usage and behaviour, but in the mobile ecosystem there is often at least one company who can see this information without using market research, and see it in much better detail. For example, a telco does not need to conduct a survey with a sample of its subscribers to find out how often they make calls or to work out how many texts they send, and how many of those texts are to international numbers. The telco has this information, for every user, without any errors.

Usage data tends to be better, cheaper, and often quicker than market research for recording what people did. It is much less powerful in working out why patterns are happening, and it is thought (by some people) to be weak in predicting what will happen if circumstances change. However, it should be noted that the advocates of big data and in particular ‘predictive analytics’ believe that it is possible to work out the answer to ‘what-if’ questions, just from usage/behaviour data.

Unique access to usage data
One limitation to the power of usage data is that in most cases only one organisation has access to a specific section of usage data. In a country with two telcos, each will only have access to the usage data for their subscribers, plus some cross-network traffic information. The owner of a website is the only company who can track the people who visit that site (* with a couple of exceptions). A bank has access to the online, mobile and other data from its customers, but not data about the users of other banks.

This unique access feature of usage data is one of the reasons why organisations buy data from other organisations and conduct market research to get a whole market picture.

* There are two exceptions to the unique access paradigm.
The first is that if users can be persuaded to download a tracking device, such as the Alexa.com toolbar, then that service will build a large, but partial picture of users of other services. This is how Alexa.com is able to estimate the traffic for the leading websites globally.

The second exception is if the service provider buys or uses a tool or service from a third party then some information is shared with that provider.

A complex and comprehensive example of this type of access is Google who sign users up to their Google services (including Android), offer web analytics to websites, and serve ads to websites, which allows them to gain a large but partial picture of online and mobile behaviour.

Legal implications of usage data
Usage data, whether it is browsing, emailing, mobile, or financial, is controlled by law in most countries, although the laws tend to vary from one jurisdiction to another. Because the scale and depth of usage data is a new phenomenon and because the tools to analyse it and the markets for selling/using it are still developing the laws are tending to lag behind the practice.

A good example, of the challenges that legislators and data owners face is determining what is permitted and what is not, are the problems that Google had in Spain and Netherlands towards the end of 2013. The Dutch Government’s Data Protection Agency ruled in November 2013 that Google had broken Dutch law by combining data together from its many services to create a holistic picture of users. Spain went one step further and fined Google 900,000 Euros for the same offence (about $1.25 million). This is unlikely to be the end of the story, the laws might change, Google might change its practices (or the permissions it collects), or the findings might be appealed. However, they illustrate that data privacy and protection are likely to create a number of challenges for data users and legislators over the next few year.

A/B testing

The definition of A/B testing is a developing and evolving one; and it is likely to evolve and expand further over the next few years. At its heart A/B testing is based on a very old principle, create a test where two offers only differ in one detail, present these two choices to matched but separate groups of people to evaluate, and whichever is the more popular is the winner.
What makes modern A/B testing different from traditional research is the tendency to evaluate the options in the real market, rather than with research participants. One high profile user of A/B testing is Google, who use it to optimise their online services. Google systematically, and in many cases automatically, select a variable, offer two options, and count the performance with real users. The winning option becomes part of the system.

Google’s A/B testing is now available to users of some of its systems, such as Google Analytics. There are also a growing range of companies offering A/B testing systems. Any service that can be readily tweaked and offered is potentially suitable for A/B testing – in particular virtual or online services.

The concept of A/B testing has moved well beyond simply testing two options and assessing the winner, for example:

Many online advertising tools allow the advertiser to submit several variations and the platform adjusts which execution is shown most often and to whom it is shown to maximise a dependent variable, for example to maximise click through.

Companies like Phillips have updated their direct mailing research/practice by developing multiple offers, e.g. 32 versions of a mailer, employing design principles to allow the differences to be assessed. The mailers are used in the market place, with a proportion of the full database, to assess their performance. The results are used in two ways. 1) The winning mailer is used for the rest of the database. 2) The performance of the different elements are assessed to create predictive analytics for future mailings.

Dynamic pricing models are becoming increasingly common in the virtual and online world. Prices in real markets, such as stock exchanges have been based for many years on dynamic pricing, but now services such as eBay, Betfair, and Amazon apply differing types of automated price matching.

Algorithmic bundling and offer development. With services that are offered virtually the components can be varied to iteratively seek combinations that work better than others.

The great strength of A/B testing is in the area of small, iterative changes, allowing organisations to optimise their products, services, and campaigns. Market research’s key strength, in this area, is the ability to research bigger changes and help suggest possible changes.

Agile development

Agile development refers to operating in ways where is it easy, quick, and cheap for the organisation to change direction and to modify products and services. One consequence of agile development is that organisations can try their product or service with the market place, rather than assessing it in advance.

Market research is of particular relevance when the costs of making a product are large, or where the consequences of launching an unsatisfactory product or service are large. But, if products and services can be created easily and the consequences of failure are low, then ‘try it and see’ can be a better option than classic forms of market research.
Whilst the most obvious place for agile development is in the area of virtual products and services, it is also used in more tangible markets. The move to print on demand books has reduced the barriers to entry in the book market and facilitated agile approaches. Don Tapscott in his book Wikinomics talks about the motorcycle market in China, which adopted an open-source approach to its design and manufacture of motorcycles, something which combined agile development and crowdsourcing (the next topic in this section).

Crowdsourcing

Crowdsourcing is being used in a wide variety of way by organisations, and several of these ways can be seen as an alternative to market research, or perhaps as routes that make market research less necessary. Key examples of crowdsourcing include:

Open source. Systems like Linux and Apache are developed collaboratively and then made freely available. The priorities for development are determined by the interaction of individuals and the community, and the success of changes is determined by a combination of peer review and market adoption.

Crowdfunding. One way of assessing whether an idea has a good chance of succeeding is to try and fund it through a crowdfunding platform, such as Kickstarter. The crowdfunding route can provide feedback, advocates, and money.

Crowdsourced product development. A great example of crowdsourcing is the T-shirt company Threadless.com. People who want to be T-shirt designers upload their designs to the website. Threadless displays these designs to the people who buy T-shirts and asks which ones people want to buy. The most popular designs are then manufactured and sold via the website. In this sort of crowdsourced model there is little need for market research as the audience get what the audience want, and the company is not paying for the designs, unless the designs prove to be successful.

Usability testing

Some market research companies offer usability testing, but there are a great many providers of this service who are not market researchers and who do not see themselves as market researchers. The field of usability testing brings together design professionals, HCI (human computer interaction), ergonomics, as well market researchers.

Usability testing for a mobile phone, or a mobile app, can include:

Scoring it against legal criteria to make sure it conforms to statutory requirements.

Scoring it against design criteria, including criteria such as disability access guidelines.

User lab testing, where potential users are given access to the product or service and are closely observed as they use it.

User testing, where potential users are given the product or given access to the service and use it for a period of time, for example two weeks. The usage may be monitored, there is often a debrief at the end of the usage period (which can be qualitative, quantitative, or both), and usage data may have been collected and analysed.

Technology or parameter driven

In some markets there are issues other than consumer choice that guide design and innovation. In areas like mobile commerce and mobile connectivity, there are legal and regulatory limits and requirements as to what can be done, so the design process will often be focused on how to maximise performance, minimise cost, whilst complying with the rules. In these situations, the guidance comes from professionals (e.g. engineers or lawyers) rather than from consumers, which reduces the role for market research.

Future innovations

This section of the chapter has looked at a wide range of approaches to gaining insight that are not strengths of market research. It is likely that this list will grow over time as technologies develop and it is likely to grow as the importance of the mobile ecosystem continues to grow.

As well as new non-market research approaches being developed it is possible, perhaps likely, that areas which are currently seen as largely or entirely the domain of market research will be shared with other non-market research companies and organisations. The growth in DIY or self-serve options in surveys, online discussions, and even whole insight communities are an indication of this direction of travel.

So, that is where the text is at the moment. Plenty of polishing still to do. But here are my questions?

Do you agree with the main points?

Have I missed any major issuies?

Are there good examples of the points I’ve made that you could suggest highlighting/using?

But, is all of this just creating a cozy world where a few thousand market researchers tweet to each other, and nobody else really contributes, reads, are even cares? The quickest way to get recognition amongst market researchers is to use the #MRX tag, so it becomes the default, and in doing so, perhaps, it becomes a fence or boundary of our own making?

Time add new links to the wider world?
Other leading #MRX figures, such as Tom Ewing and Reg Baker have written about what happens if you ignore the #MRX audience, your figures quickly decline. But perhaps the key is to be adding more dimensions to what we do, and for those dimensions to have an external focus?

By external focus, I mean using cues and clues that other people are likely to be looking for. Who outside the market researcher Twitterati would be looking for #MRX or #NewMR – even if they were looking for market research related material?

Options we might want to consider, when talking about the right subjects are:

#ROI

#Insights

#Retail

#B2B

#Mobile (we do sometimes use #MMR – mobile market research, but that does not really ‘reach out’ to the non-cognoscenti)

#BigData

#Surveys

What do you think? Is there any potential in widening the hashtags we in the #MRX chatterati use? Or, would we still be talking to the same few people?

Every week we seem to get a new report saying that Twitter, or Pinterest, or instant chat apps have knocked Facebook off its perch as the number one social media platform in the West, especially amongst younger people. One day this will be true, but not this year, nor next, nor (probably) the year after.

In partnership with Vision Critical’s Springboard omnibus I have re-run a study we first ran in August 2012, looking at social media usage, and focusing on regular social media usage. The data show two big messages:

Facebook dwarfs other social media.

The pace of change between 2012 and 2013 is glacially slow.

Table 1 shows how many people said that they had used each of the listed forms of social media in the last year. The Vision Critical Springboard omnibus is broadly representative of Great Britain, but since it is an online survey, the figures for social media exclude the (approximately) 15% of Britons who do not use the internet.

Table 1 shows that in terms of social media used in the last year, in the UK, there was very little change between 2012 and 2013, other than a drop in the claimed usage of YouTube. The gap between Facebook and the sites that are often reported to be its potential replacements, such as Twitter and Pinterest is massive, and in the case of Twitter not changing. The new messaging services, SnapChat and WeChat appear on the chart, but with very small figures.

Table 2 focuses on recent usage, asking people to say what they have used in the last week. Again the two patterns are that Facebook is a long way ahead of the others and that there are few differences between August 2012 and November 2013.

The final myth that this study crushes is the myth that young people have deserted Facebook. Table 3 looks at Facebook claimed usage (over the last year and over the last week) by three age groups. The two patterns are:

Very few differences between August 2012 and November 2013

Facebook is strongest amongst the youngest people and weakest amongst the oldest.

In terms of the fine detail, amongst the 18-24 year olds, 91% said they had used Facebook in the last year, and 84% said they had used it in the last week.

Why are so many people reporting the demise of Facebook?
Perhaps it is not for me to guess why so many people write about the demise of Facebook and the rise of alternatives, but here are a few thoughts:

Claiming new social media is beating Facebook is more newsworthy than saying “Little change”.

Some of the studies are based on stated measures such as “important to me”, rather than simply looking at behaviour.

The fastest growing phenomena are usually the smallest; small services grew at a fast rate, and the fastest growing demographic is usually the one with lowest usage rate. So, Pinterest grow faster than Facebook, but remains much smaller. The older Facebook users are the fastest growing segment, but remain the smallest group.

Data from InternetWorldStats suggest that Facebook usage in countries like the US and UK is stable. Some people are leaving Facebook, and these are broadly matched by new people joining it. In many other countries Facebook is still growing rapidly, so globally it continues to increase its user base and current dominance.

What about the future?
At some point things are going to change, they always do. But, the change is unlikely to be very soon. My prediction is that in 12 months Facebook will still be the dominant social media platform in the UK, and in most countries outside of China. I doubt that the service that will eclipse Facebook is even in widespread use in most countries at the moment. Perhaps the change will come from China, the only major place where Facebook is not a force and where there are really strong alternatives.

Updated Comment on YouTube

Annie Pettit (@LoveStats) and others have pointed at that whilst the data reported in this post show few changes in the social networks, there is a major change in the reported usage of YouTube. Claimed usage in the last year has fallen from 62% to 44%, and claimed usage in the last week has fallen from 38% to 28%.

This change requires further investigation to check, for example, the following:

Might people be under-reporting YouTube usage, perhaps because YouTube links can be embedded in so many other types of site, in particular FaceBook?

If YouTube usage has fallen, is that because fashion or tastes have moved on, or because of the advertising that seems to be every more intrusive?

How does this reported change compare with with traffic levels for YouTube?

For several years, when teaching presenting, I have been asking people to stand when they present and to adopt ‘high power’ body positions and avoid low power positions, for example not crossing your arms and legs, and not standing sideways on to the audience.

I arrived at this advice based on my own observations, tips from other trainers, and by applying learning from other fields – but there was limited, specific evidence for what I was saying.

However, I no longer need to rely on my homespun theories. Kristin Luck (a great presenter in her own right) has highlighted Amy Cuddy to me. Watch the video below, Amy Cuddy at TED, and you will understand the extent to which how you stand impacts a) how the audience receive your message, and b) the way you feel.

The ‘fake it till you make’ it part has two elements. Firstly, standing in a power position changes the chemicals in your brain to make you more confident, even though you are ‘pretending’ to be confident. Over time, you will change and you won’t be faking it. So faking it till you make it means getting a benefit in the short term and changing yourself in the mid-term.

Yesterday, at the BAQMaR Conference, the Fringe Factory launched its study into what young graduates are looking for in an industry and what is their perception of market research.

The Fringe Factory surveyed over 1800 graduates across nine countries. The report produced five “eye-catching insights and recommendations”. But for me one of the key points was that only 13% of the young people surveyed said they would consider a job in market research, and only 3% listed it as the best sector.

To find out more about the study, the Fringe Factory, and the other insights and recommendation, look at the presentation below. The presentation is hosted via SlideShare – this means you can advance the slides and by click on the four arrows in the bottom right of the presentation window, turn it into a full screen presentation.

We are all familiar with the phrase that correlation is not the same as causality, but we also know that in many cases correlation is a really good indicator that something is important – so how do we judge how much importance to give to correlation?

In the 1940s, British scientist Richard Doll conducted a study of 649 cases of lung cancer and noted that only two were non-smokers, causing him to a) stop smoking and b) to start researching the link between smoking and cancer. The correlation certainly did not prove smoking caused lung cancer. As a point on interest, in the 1940s about 80% of adults smoked, so it would have been expected that most people with lung cancer smoked. A simplistic view of correlation would have said that no action should have been taken until a cause was identified. We now know that smoking tobacco releases more than 70 different cancer causing substances.

Sometimes a correlation is useful, even when the phenomenon being measured is not a cause. Waist measurements are highly correlated with health problems, but the waist measurement does not directly cause health problems. Having too much fat tends to cause the problems and having too much fat makes the waist measurement go up. So, by measuring waist measurements we can assess likely health issues, even though the link is only correlation.

So, if we find a correlation should we ignore it until we can find a cause or mechanism, or can we act just on the correlation? The answer is, as is often the case, ‘it depends’.

When thinking about brands and marketing the key to the difference between when causality matters and doesn’t often depends on whether we are trying to tackle the underlying cause or the visible measurements. For example, it is likely, IMHO, that making your brand more relevant and having a more engaging presence will grow the number of FaceBook likes, and this is likely to be a good thing, and monitoring the likes is probably going to be a good thing. However, if the number of likes is set as a KPI, then the pressure on the managers is not to increase the engagement or salience of the brand, it is to increase the number of likes. There are many way of increasing likes that have little impact on the brand, such as running one-off promotions through to paying people to click like (typically from low-cost economies). By changing the statistic from a measure to a target we have fallen into the causality/correlation trap.

In many ways, this view of correlation and causality reflects the key point in the book Obliquity. Obliquity points out there are many things you can only achieve by not trying to achieve them, such as happiness. If a brand wants to increase its satisfaction, social engagement, or salience it can only measure it with statistics such as NPS, Likes, social media comments etc if it does not seek to directly change the numbers. Increasing your social media comments by being more newsworthy, having great products, or running wonderful campaigns is great. However, engaging a clever agency to boost your social media mentions is likely to be much less effective.

The video below shows Sue York’s presentation at the recent NewMR Training Day and you can download a PDF of the current draft. Remember, the Contemporary Answers book is intended for people new to research or new to a topic – it is not supposed to be the definitive or comprehensive answer.

We have already received some feedback (following the Training Day), but we’d love to hear more. So, please add your comments to this post using the comments facility. The mobile chapter, along with new chapters on International Research and Polling will be added in 2014.

We know that research participants sometimes cannot or will not be honest in their responses. We know about behavioral economics. We know all the things to say to encourage open and honest discussions and survey responses. But what about our online and social media-based conversations?

I’m a Second Generation Facebook user. By this I mean that I’ve been around on Facebook since almost immediately after it was released to universities in the Greater Boston Area (I’ll refrain from listing the year so you can’t do the math). What started out as a site intended to allow students to evaluate one another’s’ attractiveness has become a global commodity used for connecting, promoting, expressing, sharing, and now for market research.

One of the interesting trends that has come up in relation to social media outlets, and Facebook in particular, is something I’m going to call “mediawashing” (you heard it here first, write that word down). Similar to greenwashing, mediawashing is the dissemination of disinformation that a person chooses to put forth, typically about themselves or their lives, using social media. In laymen’s terms: people paint pretty pictures of their lives, but it’s often not the whole picture.

Numerous studies have been published demonstrating that not only is there a link between social media use and things like depression or lack of self-esteem, in some cases there is a causal effect due in large part to comparisons made between one’s real life and the bits and pieces of someone else’s life that have been shared.

A recent article in Newsweek points out how mothers on Facebook are battling increased stress and pressure thanks to constant boasting and bragging about achievements, development, and even things like a baby’s sleep habits, while conveniently failing to mention any hardships or struggles. Although most recognize logically that this is a “presentation of perfection” and is not a true reflection of reality, the effects on both the reader and the poster are dramatic. The interactions that would, at one time, impact the perceptions and behaviors of parents on a weekly or monthly basis are now happening several times a day for those who are using social networks.

This is, of course, one category of examples, but the point is clear: no one’s life is perfect. No one’s days are only filled with sunshine and rainbows and happy, helpful people who do everything they can to help a person succeed. Life is hard, and many people edit those parts out of what they choose to share.

For market researchers, this poses a particular problem when using social media and similar platforms (like communities, bulletin boards or online focus groups). While it’s true that anonymity, in these cases, offers respondents the chance to express themselves with a hopefully refreshing degree of honesty, one has to wonder: how much has mediawashing trained us to edit how we present ourselves to the world?

A measuring stick of the mediawashing effect could be the following: have you ever started to write something (a comment, a status, a tweet, a caption) on a social media platform, only to erase and begin again, in order to adjust your wording or tone? Have you ever started to write something and, in the end, decided not to post at all? Have you considered uploading a photo only to hesitate or stop because it doesn’t portray you or someone else in the best light?

As people and professionals who are dedicated to unearthing human truths from the respondents we connect with, we need to not only be aware that mediawashing exists, but to actively fight against it in our data collection processes. Whether we are scraping Facebook or Twitter or running a community, the risk that we, as researchers, only see the edited and refined version of someone’s life, their preferences, their behavior or their opinions is real. But so is the opportunity for us to allow our respondents the freedom of candid, open conversations where honesty is more valued than socially desirable responses.

So now it’s time we have an honest conversation among ourselves: how do we combat the effects of mediawashing in our research practices?

The Biggest Threat

We become irrelevant. If we don’t attract, train and develop the business leaders and thinkers of the future we will become the “typing pool” of 2010s. Businesses have changed and new business models are evolving all the time. We know business decisions are made on the basis of anything from gut feel (or someone else’s “gut feel”) to highly complex models based on more data than some of us could ever dream of getting our hands on in an entire lifetime. To help businesses make these decisions we need to be there at every step of the way. So as researchers/data scientists/marketers/leaders/entrepreneurs/gurus or whatever we want to call ourselves we need to be experts at uncovering, synthesising and most importantly communicating our ideas so that the best decisions can be made. If we continue to focus on teaching traditional narrow vocational skill sets at schools, university and in our companies we just won’t have the thinkers and doers to keep us relevant.

The Biggest Opportunity

To combine traditional MR with everything we can possibly think of to make a much smarter world. The technology we have created or have access to can collect, ask and simulate just about anything we might dream of finding out. It can also automate so much of this that the we can make decisions literally in real time or even better the decisions can be made for us. This headline from the Wall Street Journal sums it up…’Tired of Thinking? Google says we won’t have to’. Is that really true? In the new world someone has to be thinker and be able to hold on to the lateral and creative application of ideas. We can be those thinkers, we are the human element that will design the technology and the information models that will help businesses become smarter.

If you ask a consumer walking out of a supermarket why they’ve got so much junk food in their trolley (admittedly, you might want to rephrase that rather than sounding like you’re accusing them, or they’ll never stop to do an interview), they’ll probably give you a rational, and likely very plausible, reason. Probably something like ‘ready meals are convenient’, or ‘the kids like them’, or ‘I was in a hurry’. And to some extent, this might be true, but what they probably won’t be able to tell you is that if they were hungry when they got to the supermarket, the chances are that their hunger, a ‘hot’ – or emotional – state, took over and they probably would have stuck more closely to their list if they’d gone shopping on a full stomach.

We’ve all been there – we’ve skipped lunch and someone passes by our desk with a cake which is so much more tempting when we’re hungry, even if we’re trying to watch what we eat. Giving in to temptation is part of what makes us human, as we’re often (and more than we might think) governed by our emotions.

By the same token, a consumer ordering a meal in a restaurant probably won’t be aware of the anchors within the menu design which affect their choice – like the really expensive steak which makes the standard (lower priced) steak look significantly better value in comparison, or the way they’ve gone for the second least expensive wine, because they’re using price as a heuristic for quality (but don’t want to spend too much or look too flashy with the dearest wine). And it’s not just us mere mortals who aren’t as rational as we might expect; even those paid big bucks to made fair, rational and sensible decisions have the same foibles – including (scarily) judges.

We’re at an interesting turning point for the industry where our understanding of the multiple layers of influence on consumers continues to advance at a rate of knots. Meanwhile, the technology available, especially mobile devices and other tools like wearable cameras, give us more opportunities than ever to get closer to decisions as they’re being made. Joining the dots of this knowledge and tech enables a much better appreciation of the way our minds are influenced, from the people we’re with, to the physical (or digital) environment or the way we’re feeling at the time events are unfolding, and leads us to a place where decision making starts to make a lot more sense, and so becomes more insightful and actionable.

So the opportunities are all about understanding what ingredients we need to use, and then tweaking the recipe to get it right. The basic recipe contains some combination of a big dollop of context, a cup of observation, a handful of self-reflection and a pinch of seasoning, but needs to be adapted according to taste. Then it’s about giving it a big stir and baking the mixture until it’s cooked right through. We shouldn’t be afraid to try new ingredients, or experiment with different flavour combinations to see what works best, as the best chefs will tell you. (Well probably. I don’t actually know any chefs. But I know a lot of other experimenters who would agree, although they don’t work in cake-related industries and the metaphor falls down, so I’ve made an assumption.)

If the bake is the analysis, then we need to make sure the decoration is top notch too. We need to present a beautiful cake, a beautiful story for our clients. A cake so tempting it makes our audience want to devour it, without having to worry about being in a hot state or watching their weight – it’s not a real cake after all. Or maybe it is, I’ve never tried making an actual debrief cake, the closest I’ve been is taking mince pies to a presentation, maybe I’ll put that on my list of things to try…
And we’d do well to remember that we don’t stop being emotional decision makers when we walk into work in the morning, which is why creating stories with our research findings is so much more impactful than boring the pants off everyone with detail and 200 charts. (I’ve already ranted about that recently, so I won’t go over it again here.) Otherwise it’d be like baking a fantastic cake, jam packed with flavour and punch, that no-one will want to go near. And what a waste that would be.

Unfortunately, there are lots of yukky-looking cakes out there. The challenge is to create one our clients want to eat. Let’s bake!

At the risk of mixing my metaphors too much, that’s pretty much our philosophy at Join the Dots – we mix up different ingredients – or dots, if you will – to understand the bigger picture. Hence our name. So whatever your metaphor – whether it’s baking, dots, or something else – whatever you do, join them up, and when you’ve done that, don’t forget the icing on the cake.